Metal　ion　is　an　indispensable　factor　for　the　proper　folding,　structural　stability　and　functioning　of　RNA　molecules.　However,　it　is　very　difficult　for　experimental　methods　to　detect　them　in　RNAs.　With　the　increase　of　experimentally　resolved　RNA　structures,　it　becomes　possible　to　identify　the　metal　ion-binding　sites　in　RNA　structures　through　in-silico　methods.　Here,　we　propose　an　approach　called　Metal3DRNA　to　identify　the　binding　sites　of　the　most　common　metal　ions　(Mg2+,　Na+　and　K+)　in　RNA　structures　by　using　a　three-dimensional　convolutional　neural　network　model.　The　negative　samples,　screened　out　based　on　the　analysis　for　binding　surroundings　of　metal　ions,　are　more　like　positive　ones　than　the　randomly　selected　ones,　which　are　beneficial　to　a　powerful　predictor　construction.　The　microenvironments　of　the　spatial　distributions　of　C,　O,　N　and　P　atoms　around　a　sample　are　extracted　as　features.　Metal3DRNA　shows　a　promising　prediction　power,　generally　surpassing　the　state-of-the-art　methods　FEATURE　and　MetalionRNA.　Finally,　utilizing　the　visualization　method,　we　inspect　the　contributions　of　nucleotide　atoms　to　the　classification　in　several　cases,　which　provides　a　visualization　that　helps　to　comprehend　the　model.　The　method　will　be　helpful　for　RNA　structure　prediction　and　dynamics　simulation　study.